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Lab robot uses innovative information technology to find new drugs

23 February 2009

A robot scientist that can make informed guesses about how effective
different chemical compounds will be at fighting different diseases
could revolutionise the pharmaceutical industry by developing more
effective treatments more cheaply and quickly than current methods.

The robot, known as Eve, uses advanced artificial intelligence
combined with innovative data mining and knowledge discovery techniques
to analyse the results of pharmacological experiments it conducts
itself. Eve is being developed under the EU-funded IQ project.

By relating the chemical structure of different compounds to their
pharmacological activity, Eve is able to learn which chemical compounds
should be tested next, bringing a degree of predictability to drug
screening procedures that, until now, have tended to be a bit hit and
miss.

“Over time, Eve will learn to pick out the chemical compounds that
are likely to be most effective against a certain target by analysing
data from past experiments and comparing chemical structures to their
pharmacological properties,” explains Saso Dzeroski, a researcher at the
Jozef Stefan Institute in Ljubljana who helped develop Eve’s data mining
capabilities.

“That should help scientists and pharmaceutical companies identify
more effective compounds to treat different diseases, allowing them to
find drug leads in a fraction of the time and at a fraction of the cost
of current methods.”

Eve could minimise the need for random testing of chemical compounds,
Dzeroski says, noting that the robot scientist is the first computer
system capable of originating its own experiments, physically performing
them, interpreting the results and then repeating the cycle.

Currently, when a new drug is sought pharmacological researchers
conduct a blind study of tens or hundreds of thousands of chemical
compounds, applying them to an assay for a disease. The results of those
tests determine the so-called Quantitative Structure-Activity
Relationships (QSARs) that relate the structure of a chemical compound
to its pharmacological activity.

Exhaustive testing like this is time-consuming, costly and generally
has to be repeated each time a new drug is sought.
More “intelligent” approach to drug discovery

Eve offers a more “intelligent” approach, says Ross King, a computer
science researcher at the University of Wales, Aberystwyth where Eve is
to be installed.

The robot conducts the QSAR testing in assays itself, analyses the
results and stores the data for future use. Over the course of numerous
experiments, Eve learns which chemical structures are likely to be
effective in specific assays. So, instead of choosing compounds to test
at random, it can pick ones that are more likely to be effective.

“We have carried out some preliminary trials and the compounds picked
by Eve show more promise than those selected randomly,” Dzeroski says.

New data mining techniques developed by a team of researchers led by
Dzeroski lie at the heart of Eve’s groundbreaking drug discovery
capabilities. Working in the EU-funded IQ project, the team developed
new methods to analyse complex data, including chemical structures, from
databases such as that in which Eve stores the results of its
experiments.

Unlike most data mining approaches, in which an individual analysis
is carried out on a single dataset, such as a spreadsheet, the
techniques developed in the IQ project allow knowledge discovery
processes, consisting of several analysis steps, to be carried out
across multiple sets of complex data.

The techniques rely on the use of so-called inductive databases that
contain not only raw data but also information about patterns and models
valid in the data. In the case of drug discovery, the structures of the
chemical compounds tested and their effectiveness would be the raw data,
while molecular structures that appear commonly in effective compounds
would be patterns, and the equations that predict a compound’s
effectiveness would be models.

From experimental data collected by Eve, patterns would emerge that
can then be used to make informed guesses about which compounds should
be effective and which probably will not be. The same data mining
techniques are also being applied by the IQ project partners in other
fields, including genomics, systems biology and environmental sciences.

“Because much more than raw data is being analysed, the same process
for identifying different patterns can be reused, regardless of whether
you are trying to develop a drug to treat AIDS or tuberculosis,”
Dzeroski explains.

Eve will initially be put to work at the University of Wales to
search for compounds that could be effective in treating malaria and
schistosomiasis, so-called Third World diseases that are the focus of
only limited research by commercial drug companies.

King says their mission is both to demonstrate that the data mining
technology works and to find new leads that could result in new drugs
being developed in the future.

Dzeroski foresees more robots like Eve being put to use in research
labs and drug companies over the coming years. And although it will take
10 to 15 years for new drugs, based on compounds picked out by Eve, to
start being used in treatments, the work done now “could have a major
impact on the pharmaceutical industry and on healthcare in general in
the future,” he says.

The IQ project received funding under the FET - Open scheme of the
EU’s Sixth Framework Programme for research.